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Design of Incremental FCM-based Recursive RBF Neural Networks Pattern Classifier for Big Data Processing
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 Title & Authors
Design of Incremental FCM-based Recursive RBF Neural Networks Pattern Classifier for Big Data Processing
Lee, Seung-Cheol; Oh, Sung-Kwun;
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 Abstract
In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.
 Keywords
Incremental fuzzy C-Means;Recursive least square estimation;RBF neural networks;
 Language
Korean
 Cited by
 References
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